Facial landmark localization by enhanced convolutional neural network

Deng, Weihong; Fang, Yuke; Xu, Zexiang; Hu, Jiani

doi:10.1016/j.neucom.2017.07.052

Cited by 25 publications

(6 citation statements)

References 9 publications

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“…In order to prevent multiple neurons in the network from learning the same content and improve the training effect, before network training. As there is no uniform standard for the selection of parameters in the network, and the network models constructed based on different data sets will have different training effects on the selection of different network parameters, it is necessary to select a suitable network according to the comparison and analysis of the actual data set [ 20 ]. Active local reduction of rock strata combination is suitable for when the number of layers is small, various rock stratum combination reduction calculations can be performed, and the obtained results can reflect the slope deformation controlled by different rock layers on the entire slope different forms of destruction ( Figure 7 ).…”

Section: Engineering Application and Results Analysismentioning

confidence: 99%

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Liu¹,

Yan²,

Zheng³

2022

Computational Intelligence and Neuroscience

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The geological mechanics, geotechnical characteristics, and hydrogeological conditions of slopes are complex and changeable, so their stability assessment is a complicated system; their traditional engineering geological assessment does not consider the opposition of the system, the uncertainty of performance indicators, and the ambiguity of index classification, being easy to distort results due to the ambiguity. Improved convolutional neural network (CNN) has outstanding advantages in analyzing problems with randomness and fuzziness. It can perform unified numerical processing on slope assessment indicators with precise values, interval values, and qualitative judgment values, making the traditional qualitative description is transformed into quantitative calculation. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the comprehensive assessment model for slope stability and engineering geology; elaborated the development background, current status, and future challenges of the improved CNN; introduced the methods and principles of the model structure, convolutional layer design, and data flow optimization of the improved CNN; performed the assessment index system establishment and index weight determination; established the mathematical assessment model for slope stability; conducted the assessment module design for slope stability based on the improved CNN; analysed the importance of individual factors to the comprehensive engineering geological characteristics; discussed the determination of assessment value of comprehensive unit engineering geological characteristics; explored the assessment module design for slope engineering geology based on the improved CNN; and finally carried out an engineering application and its result analysis. The study results show that the improved CNN can select some universal and objective factors according to the actual conditions of slopes, including topography, stratum lithology, geological structure, atmospheric rainfall, groundwater, engineering activities, setting up factor sets and judgment sets, and making fuzzy inferences. The comprehensive assessment model can use appropriate mathematical methods to judge the pros and cons of slope’s stability and engineering geology according to certain principles and standards, and grade the results and identify the most important geological problems. The results of this paper provide a reference for further researches on the design of a comprehensive assessment model for slope stability and engineering geology based on the improved CNN.

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Section: Engineering Application and Results Analysismentioning

confidence: 99%

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Liu¹,

Yan²,

Zheng³

2022

Computational Intelligence and Neuroscience

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“…We used Stochastic Gradient Descent with Momentum for training the CNN. The value of momentum is 0.9 inspired by [29]- [31]. The maximum number of epoch is set to 50 to ensure network convergence.…”

Section: Resultsmentioning

confidence: 99%

Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?

Jamil¹,

Almisreb²,

Ariffin³

et al. 2018

IJEECS

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Current deep convolution neural network (CNN) has shown to achieve superior performance on a number of computer vision tasks such as image recognition, classification and object detection. The deep network was also tested for view-invariance, robustness and illumination invariance. However, the CNN architecture has thus far only been tested on non-uniform illumination invariant. Can CNN perform equally well for very underexposed or overexposed images or known as uniform illumination invariant? This is the gap that we are addressing in this paper. In our work, we collected ear images under different uniform illumination conditions with lumens or lux values ranging from 2 lux to 10,700 lux. A total of 1,100 left and right ear images from 55 subjects are captured under natural illumination conditions. As CNN requires considerably large amount of data, the ear images are further rotated at every 5o angles to generate 25,300 images. For each subject, 50 images are used as validation/testing dataset, while the remaining images are used as training datasets. Our proposed CNN model is then trained from scratch and validation and testing results showed recognition accuracy of 97%. The results showed that 100% accuracy is achieved for images with lumens ranging above 30 but having problem with lumens less than 10 lux

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“…Several deep learning models have adopted this approach, reporting state-of-the arts results in speech enhancement and vision regression problems. 28,29 Among those papers reporting Raman concentration predictions using CNNs, the focus has so far been on returning the concentration of a single analyte of interest 30,31 rather than obtaining a complete spectral model. Thus, multi-component regression, particularly when the spectral model is incomplete (i.e.…”

Section: Introductionmentioning

confidence: 99%

Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks

Boateng

Dai

et al. 2022

Analyst

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Facial landmark localization by enhanced convolutional neural network

Cited by 25 publications

References 9 publications

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Design of a Comprehensive Assessment Model for the Stability and Engineering Geology of Slope Based on Improved Convolutional Neural Network

Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?

Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks

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